PET Head Motion Estimation Using Supervised Deep Learning with Attention [0.03%]
基于注意力的监督深度学习估计PET头动技术研究
Zhuotong Cai,Tianyi Zeng,Jiazhen Zhang et al.
Zhuotong Cai et al.
Head movement poses a significant challenge in brain positron emission tomography (PET) imaging, resulting in image artifacts and tracer uptake quantification inaccuracies. Effective head motion estimation and correction are crucial for pre...
CiSeg: Unsupervised Cross-Modality Adaptation for 3D Medical Image Segmentation via Causal Intervention [0.03%]
基于因果干预的无监督跨模态适应的3D医学图像分割方法
Peiqing Lv,Yaonan Wang,Min Liu et al.
Peiqing Lv et al.
Unsupervised domain adaptation (UDA) addresses the domain shift problem by transferring knowledge from labeled source domain data (e.g. CT) to unlabeled target domain data (e.g. MRI). While state-of-the-art methods reduce domain gaps via im...
Overlap-Aware Online-Adaptive Non-Rigid Registration of Intraoperative Tissue in Minimally Invasive Surgery [0.03%]
一种用于微创手术术中组织非刚性配准的在线自适应重叠感知法
Hangjie Mo,Weizhao Cheng,Ziming Shen et al.
Hangjie Mo et al.
Non-rigid registration of intraoperative tissue is essential for surgical navigation and scene reconstruction in minimally invasive surgery. However, accurate registration remains challenging due to significant tissue deformation and partia...
Deep Few-view High-resolution Photon-counting CT at Halved Dose for Extremity Imaging [0.03%]
用于四肢成像的低剂量高分辨光子计数CT
Mengzhou Li,Chuang Niu,Ge Wang et al.
Mengzhou Li et al.
X-ray photon-counting computed tomography (PCCT) for extremity allows multi-energy high-resolution (HR) imaging but its radiation dose can be further improved. Despite the great potential of deep learning techniques, their application in HR...
Unsupervised High-Order Implicit Neural Representation with Line Attention for Metal Artifact Reduction [0.03%]
基于线注意力的无监督高阶隐式神经表示金属伪影减少方法
Hongyu Chen,Shaoguang Huang,Wei He et al.
Hongyu Chen et al.
The presence of metallic implants introduces bright and dark streaks that appear in computed tomography (CT) images, degrading image quality and interfering with medical diagnosis. To reduce these artifacts, deep learning approaches have be...
Source-Free Active Domain Adaptation via Influential-Points-Guided Progressive Teacher for Medical Image Segmentation [0.03%]
基于有影响力的点引导的渐进式教师的无源域适应主动领域适应方法在医学图像分割中的应用研究
Yong Chen,Xiangde Luo,Renyi Chen et al.
Yong Chen et al.
Domain adaptation in medical image segmentation enables pre-trained models to generalize to new target domains. Given limited annotated data and privacy constraints, Source-Free Active Domain Adaptation (SFADA) methods provide promising sol...
Detailed delineation of the fetal brain in diffusion MRI via multi-task learning [0.03%]
基于多任务学习的胎儿大脑弥散张量纤维束分割研究
Davood Karimi,Camilo Calixto,Haykel Snoussi et al.
Davood Karimi et al.
Diffusion-weighted MRI (dMRI) is increasingly used to study the normal and abnormal development of fetal brain in-utero. It offers invaluable insights into the neurodevelopmental processes in the fetal stage. However, reliable analysis of f...
Physics-guided Variational Method for Fractional Flow Reserve Based on Coronary Angiography [0.03%]
基于冠状动脉造影的物理指导变分法计算分数流储备值模拟研究
Qi Zhang,Heye Zhang,Zhifan Gao et al.
Qi Zhang et al.
As a leading global cause of mortality, coronary ischemia requires accurate diagnostics for effective management. The combining coronary angiography with fractional flow reserve (FFR) offers structural and functional assessment of coronary ...
Q-space Guided Multi-Modal Translation Network for Diffusion-Weighted Image Synthesis [0.03%]
基于Q空间的多模态翻译网络的扩散加权图合成方法
Pengli Zhu,Yingji Fu,Nanguang Chen et al.
Pengli Zhu et al.
Diffusion-weighted imaging (DWI) enables non-invasive characterization of tissue microstructure, yet acquiring densely sampled q-space data remains time-consuming and impractical in many clinical settings. Existing deep learning methods are...
MACE Risk Prediction in ARVC Patients via CMR: A Three-Tier Spatiotemporal Transformer with Pericardial Adipose Tissue Embedding [0.03%]
基于CMR的ARVC患者MACE风险预测：具有心包脂肪组织嵌入的三阶段时空变换器方法
Xiaoyu Wang,Jinyu Zheng,Chaolu Feng et al.
Xiaoyu Wang et al.
Major adverse cardiac events (MACE) pose a high life-threatening risk to patients with arrhythmogenic right ventricular cardiomyopathy (ARVC). Cardiac magnetic resonance (CMR) has been proven to reflect the risk of MACE, but two challenges ...